Tumor cell heterogeneity and its transcriptional bases in pancreatic cancer: a tale of two cell types and their many variants.

Pancreatic ductal adenocarcinoma (PDAC), one of the most highly lethal tumors, is characterized by complex histology, with a massive fibrotic stroma in which both pseudo-glandular structures and compact nests of abnormally differentiated tumor cells are embedded, in different proportions and with different mutual relationships in space. This complexity and the heterogeneity of the tumor component have hindered the development of a broadly accepted, clinically actionable classification of PDACs, either on a morphological or a molecular basis. Here, we discuss evidence suggesting that such heterogeneity can to a large extent, albeit not exclusively, be traced back to two main classes of PDAC cells that commonly coexist in the same tumor: cells that maintained their ability to differentiate toward endodermal, mucin-producing epithelia and epithelial cells unable to form glandular structures and instead characterized by various levels of squamous differentiation and the expression of mesenchymal lineage genes. The underlying gene regulatory networks and how they are controlled by distinct transcription factors, as well as the practical implications of these two different populations of tumor cells, are discussed.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Post-diagnosis dietary factors, supplement use and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis.

The role of diet in colorectal cancer prognosis is not well understood and specific lifestyle recommendations are lacking. We searched for randomised controlled trials (RCTs) and longitudinal observational studies on post-diagnosis dietary factors, supplement use and colorectal cancer survival outcomes in PubMed and Embase from inception until 28th February 2022. Random-effects dose-response meta-analyses were conducted when at least three studies had sufficient information. The evidence was interpreted and graded by the CUP Global independent Expert Committee on Cancer Survivorship and Expert Panel. Five RCTs and 35 observational studies were included (30,242 cases, over 8700 all-cause and 2100 colorectal cancer deaths, 3700 progression, recurrence, or disease-free events). Meta-analyses, including 3-10 observational studies each, were conducted for: whole grains, nuts/peanuts, red and processed meat, dairy products, sugary drinks, artificially sweetened beverages, coffee, alcohol, dietary glycaemic load/index, insulin load/index, marine omega-3 polyunsaturated fatty acids, supplemental calcium, circulating 25-hydroxyvitamin D (25[OH]D) and all-cause mortality; for alcohol, supplemental calcium, circulating 25(OH)D and colorectal cancer-specific mortality; and for circulating 25(OH)D and recurrence/disease-free survival. The overall evidence was graded as 'limited'. The inverse associations between healthy dietary and/or lifestyle patterns (including diets that comprised plant-based foods), whole grains, total, caffeinated, or decaffeinated coffee and all-cause mortality and the positive associations between unhealthy dietary patterns, sugary drinks and all-cause mortality provided 'limited-suggestive' evidence. All other exposure-outcome associations provided 'limited-no conclusion' evidence. Additional, well-conducted cohort studies and carefully designed RCTs are needed to develop specific lifestyle recommendations for colorectal cancer survivors.

Post-diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis.

The adiposity influence on colorectal cancer prognosis remains poorly characterised. We performed a systematic review and meta-analysis on post-diagnosis adiposity measures (body mass index [BMI], waist circumference, waist-to-hip ratio, weight) or their changes and colorectal cancer outcomes. PubMed and Embase were searched through 28 February 2022. Random-effects meta-analyses were conducted when at least three studies had sufficient information. The quality of evidence was interpreted and graded by the Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel. We reviewed 124 observational studies (85 publications). Meta-analyses were possible for BMI and all-cause mortality, colorectal cancer-specific mortality, and cancer recurrence/disease-free survival. Non-linear meta-analysis indicated a reverse J-shaped association between BMI and colorectal cancer outcomes (nadir at BMI 28 kg/m2). The highest risk, relative to the nadir, was observed at both ends of the BMI distribution (18 and 38 kg/m2), namely 60% and 23% higher risk for all-cause mortality; 95% and 26% for colorectal cancer-specific mortality; and 37% and 24% for cancer recurrence/disease-free survival, respectively. The higher risk with low BMI was attenuated in secondary analyses of RCTs (compared to cohort studies), among studies with longer follow-up, and in women suggesting potential methodological limitations and/or altered physiological state. Descriptively synthesised studies on other adiposity-outcome associations of interest were limited in number and methodological quality. All the associations were graded as limited (likelihood of causality: no conclusion) due to potential methodological limitations (reverse causation, confounding, selection bias). Additional well-designed observational studies and interventional trials are needed to provide further clarification.

Post-diagnosis adiposity, physical activity, sedentary behaviour, dietary factors, supplement use and colorectal cancer prognosis: Global Cancer Update Programme (CUP Global) summary of evidence grading.

Based on the World Cancer Research Fund Global Cancer Update Programme, we performed systematic reviews and meta-analyses to investigate the association of post-diagnosis adiposity, physical activity, sedentary behaviour, and dietary factors with colorectal cancer prognosis. We searched PubMed and Embase until 28th February, 2022. An independent expert committee and expert panel graded the quality of evidence. A total of 167 unique publications were reviewed, and all but five were observational studies. The quality of the evidence was graded conservatively due to the high risk of several biases. There was evidence of non-linearity in the associations between body mass index and colorectal cancer prognosis. The associations appeared reverse J-shaped, and the quality of this evidence was graded as limited (likelihood of causality: limited-no conclusion). The evidence on recreational physical activity and lower risk of all-cause mortality (relative risk [RR] highest vs. lowest: 0.69, 95% confidence interval [CI]: 0.62-0.77) and recurrence/disease-free survival (RR: 0.80, 95% CI: 0.70-0.92) was graded as limited-suggestive. There was limited-suggestive evidence for the associations between healthy dietary and/or lifestyle patterns (including diets that comprised plant-based foods), intake of whole grains and coffee with lower risk of all-cause mortality, and between unhealthy dietary patterns and intake of sugary drinks with higher risk of all-cause mortality. The evidence for other exposures on colorectal cancer outcomes was sparse and graded as limited-no conclusion. Analyses were conducted excluding cancer patients with metastases without substantial changes in the findings. Well-designed intervention and cohort studies are needed to support the development of lifestyle recommendations for colorectal cancer patients.

Post-diagnosis physical activity and sedentary behaviour and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis.

Low physical activity and high sedentary behaviour have been clearly linked with colorectal cancer development, yet data on their potential role in colorectal cancer survival is limited. Better characterisation of these relationships is needed for the development of post-diagnosis physical activity and sedentary behaviour guidance for colorectal cancer survivors. We searched PubMed and Embase through 28 February 2022 for studies assessing post-diagnosis physical activity, and/or sedentary behaviour in relation to all-cause and cause-specific mortality and recurrence after colorectal cancer diagnosis. Total and recreational physical activity were assessed overall and by frequency, duration, intensity, and volume using categorical, linear, and non-linear dose-response random-effects meta-analyses. The Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel interpreted and graded the likelihood of causality. We identified 16 observational studies on 82,220 non-overlapping patients from six countries. Physical activity was consistently inversely associated with colorectal cancer morbidity and mortality outcomes, with 13%-60% estimated reductions in risk. Sedentary behaviour was positively associated with all-cause mortality. The evidence had methodological limitations including potential confounding, selection bias and reverse causation, coupled with a limited number of studies for most associations. The CUP Global Expert panel concluded limited-suggestive evidence for recreational physical activity with all-cause mortality and cancer recurrence. Total physical activity and its specific domains and dimensions, and sedentary behaviour were all graded as limited-no conclusion for all outcomes. Future research should focus on randomised trials, while observational studies should obtain objective and repeated physical activity measures and better adjustment for confounders.

Factors Affecting Nonfunctioning Small Pancreatic Neuroendocrine Neoplasms and Proposed New Treatment Strategies.

BACKGROUND & AIMS: Despite previously reported treatment strategies for nonfunctioning small (≤20 mm) pancreatic neuroendocrine neoplasms (pNENs), uncertainties persist. We aimed to evaluate the surgically resected cases of nonfunctioning small pNENs (NF-spNENs) in a large Japanese cohort to elucidate an optimal treatment strategy for NF-spNENs. METHODS: In this Japanese multicenter study, data were retrospectively collected from patients who underwent pancreatectomy between January 1996 and December 2019, were pathologically diagnosed with pNEN, and were treated according to the World Health Organization 2019 classification. Overall, 1490 patients met the eligibility criteria, and 1014 were included in the analysis cohort. RESULTS: In the analysis cohort, 606 patients (59.8%) had NF-spNENs, with 82% classified as grade 1 (NET-G1) and 18% as grade 2 (NET-G2) or higher. The incidence of lymph node metastasis (N1) by grade was significantly higher in NET-G2 (G1: 3.1% vs G2: 15.0%). Independent factors contributing to N1 were NET-G2 or higher and tumor diameter ≥15 mm. The predictive ability of tumor size for N1 was high. Independent factors contributing to recurrence included multiple lesions, NET-G2 or higher, tumor diameter ≥15 mm, and N1. However, the independent factor contributing to survival was tumor grade (NET-G2 or higher). The appropriate timing for surgical resection of NET-G1 and NET-G2 or higher was when tumors were >20 and >10 mm, respectively. For neoplasms with unknown preoperative grades, tumor size >15 mm was considered appropriate. CONCLUSIONS: NF-spNENs are heterogeneous with varying levels of malignancy. Therefore, treatment strategies based on tumor size alone can be unreliable; personalized treatment strategies that consider tumor grading are preferable.

Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms "in the Wild".

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)-based algorithms employing deep learning have shown their ability to match pathologists' performance in assigning Gleason scores, with the potential to enhance pathologists' grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.

Digital and Computational Pathology Applications in Bladder Cancer: Novel Tools Addressing Clinically Pressing Needs.

Bladder cancer (BC) remains a major disease burden in terms of incidence, morbidity, mortality, and economic cost. Deciphering the intrinsic molecular subtypes and identification of key drivers of BC has yielded successful novel therapeutic strategies. Advances in computational and digital pathology are reshaping the field of anatomic pathology. This review offers an update on the most relevant computational algorithms in digital pathology that have been proposed to enhance bladder cancer management. These tools promise to enhance diagnostics, staging and grading accuracy, and streamline efficiency while advancing practice consistency. Computational applications that enable intrinsic molecular classification, predict response to neoadjuvant therapy, and identify targets of therapy are also reviewed.

Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma.

The new grading system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups on the basis of histologic patterns observed on surgical specimens. This study sought to provide novel insights into the IASLC grading system, with particular focus on recurrence-specific survival (RSS) and lung cancer-specific survival among patients with stage I adenocarcinoma. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, and complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified into a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 (29%) of 1443 patients with grade 1 or 2 tumors classified based on the predominant pattern-based grading system had their tumors upgraded to grade 3 based on the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank P < .001) but not from those for patients with predominant high-grade patterns (P = .3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and lung cancer-specific survival after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern-based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.

Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer.

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.

The impact of urgency of repair on outcomes following thoracic endovascular aortic repair for blunt thoracic aortic injury.

OBJECTIVE: Current societal recommendations regarding the timing of thoracic endovascular aortic repair (TEVAR) for blunt thoracic aortic injury (BTAI) vary. Prior studies have shown that elective repair was associated with lower mortality after TEVAR for BTAI. However, these studies lacked data such as Society for Vascular Surgery (SVS) aortic injury grades and TEVAR-related postoperative outcomes. Therefore, we used the Vascular Quality Initiative registry, which includes relevant anatomic and outcome data, to examine the outcomes following urgent/emergent (≤ 24 hours) vs elective TEVAR for BTAI. METHODS: Patients undergoing TEVAR for BTAI between 2013 and 2022 were included, excluding those with SVS grade 4 aortic injuries. We included covariates such as age, sex, race, transfer status, body mass index, preoperative hemoglobin, comorbidities, medication use, SVS aortic injury grade, coexisting injuries, Glasgow Coma Scale, and prior aortic surgery in a regression model to compute propensity scores for assignment to urgent/emergent or elective TEVAR. Perioperative outcomes and 5-year mortality were evaluated using inverse probability-weighted logistic regression and Cox regression, also adjusting for left subclavian artery revascularization/occlusion and annual center and physician volumes. RESULTS: Of 1016 patients, 102 (10%) underwent elective TEVAR. Patients who underwent elective repair were more likely to undergo revascularization of the left subclavian artery (31% vs 7.5%; P < .001) and receive intraoperative heparin (94% vs 82%; P = .002). After inverse probability weighting, there was no association between TEVAR timing and perioperative mortality (elective vs urgent/emergent: 3.9% vs 6.6%; odds ratio [OR], 1.1; 95% confidence interval [CI], 0.27-4.7; P = .90) and 5-year mortality (5.8% vs 12%; hazard ratio [HR], 0.95; 95% CI, 0.21-4.3; P > .9).Compared with urgent/emergent TEVAR, elective repair was associated with lower postoperative stroke (1.0% vs 2.1%; adjusted OR [aOR], 0.12; 95% CI, 0.02-0.94; P = .044), even after adjusting for intraoperative heparin use (aOR, 0.12; 95% CI, 0.02-0.92; P = .042). Elective TEVAR was also associated with lower odds of failure of extubation immediately after surgery (39% vs 65%; aOR, 0.18; 95% CI, 0.09-0.35; P < .001) and postoperative pneumonia (4.9% vs 11%; aOR, 0.34; 95% CI, 0.13-0.91; P = .031), but comparable odds of any postoperative complication as a composite outcome and reintervention during index admission. CONCLUSIONS: Patients with BTAI who underwent elective TEVAR were more likely to receive intraoperative heparin. Perioperative mortality and 5-year mortality rates were similar between the elective and emergent/urgent TEVAR groups. Postoperatively, elective TEVAR was associated with lower ischemic stroke, pulmonary complications, and prolonged hospitalization. Future modifications in society guidelines should incorporate the current evidence supporting the use of elective TEVAR for BTAI. The optimal timing of TEVAR in patients with BTAI and the factors determining it should be the subject of future study to facilitate personalized decision-making.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF? SUMMARY ANSWER: The BlastAssist pipeline can measure a comprehensive set of interpretable features of human embryos and either outperform or perform comparably to embryologists and human experts in measuring these features. WHAT IS KNOWN ALREADY: Some studies have applied deep learning and developed 'black-box' algorithms to predict embryo viability directly from microscope images and videos but these lack interpretability and generalizability. Other studies have developed deep learning networks to measure individual features of embryos but fail to conduct careful comparisons to embryologists' performance, which are fundamental to demonstrate the network's effectiveness. STUDY DESIGN, SIZE, DURATION: We applied the BlastAssist pipeline to 67 043 973 images (32 939 embryos) recorded in the IVF lab from 2012 to 2017 in Tel Aviv Sourasky Medical Center. We first compared the pipeline measurements of individual images/embryos to manual measurements by human experts for sets of features, including: (i) fertilization status (n = 207 embryos), (ii) cell symmetry (n = 109 embryos), (iii) degree of fragmentation (n = 6664 images), and (iv) developmental timing (n = 21 036 images). We then conducted detailed comparisons between pipeline outputs and annotations made by embryologists during routine treatments for features, including: (i) fertilization status (n = 18 922 embryos), (ii) pronuclei (PN) fade time (n = 13 781 embryos), (iii) degree of fragmentation on Day 2 (n = 11 582 embryos), and (iv) time of blastulation (n = 3266 embryos). In addition, we compared the pipeline outputs to the implantation results of 723 single embryo transfer (SET) cycles, and to the live birth results of 3421 embryos transferred in 1801 cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS: In addition to EmbryoScope™ image data, manual embryo grading and annotations, and electronic health record (EHR) data on treatment outcomes were also included. We integrated the deep learning networks we developed for individual features to construct the BlastAssist pipeline. Pearson's χ2 test was used to evaluate the statistical independence of individual features and implantation success. Bayesian statistics was used to evaluate the association of the probability of an embryo resulting in live birth to BlastAssist inputs. MAIN RESULTS AND THE ROLE OF CHANCE: The BlastAssist pipeline integrates five deep learning networks and measures comprehensive, interpretable, and quantitative features in clinical IVF. The pipeline performs similarly or better than manual measurements. For fertilization status, the network performs with very good parameters of specificity and sensitivity (area under the receiver operating characteristics (AUROC) 0.84-0.94). For symmetry score, the pipeline performs comparably to the human expert at both 2-cell (r = 0.71 ± 0.06) and 4-cell stages (r = 0.77 ± 0.07). For degree of fragmentation, the pipeline (acc = 69.4%) slightly under-performs compared to human experts (acc = 73.8%). For developmental timing, the pipeline (acc = 90.0%) performs similarly to human experts (acc = 91.4%). There is also strong agreement between pipeline outputs and annotations made by embryologists during routine treatments. For fertilization status, the pipeline and embryologists strongly agree (acc = 79.6%), and there is strong correlation between the two measurements (r = 0.683). For degree of fragmentation, the pipeline and embryologists mostly agree (acc = 55.4%), and there is also strong correlation between the two measurements (r = 0.648). For both PN fade time (r = 0.787) and time of blastulation (r = 0.887), there's strong correlation between the pipeline and embryologists. For SET cycles, 2-cell time (P < 0.01) and 2-cell symmetry (P < 0.03) are significantly correlated with implantation success rate, while other features showed correlations with implantation success without statistical significance. In addition, 2-cell time (P < 5 × 10-11), PN fade time (P < 5 × 10-10), degree of fragmentation on Day 3 (P < 5 × 10-4), and 2-cell symmetry (P < 5 × 10-3) showed statistically significant correlation with the probability of the transferred embryo resulting in live birth. LIMITATIONS, REASONS FOR CAUTION: We have not tested the BlastAssist pipeline on data from other clinics or other time-lapse microscopy (TLM) systems. The association study we conducted with live birth results do not take into account confounding variables, which will be necessary to construct an embryo selection algorithm. Randomized controlled trials (RCT) will be necessary to determine whether the pipeline can improve success rates in clinical IVF. WIDER IMPLICATIONS OF THE FINDINGS: BlastAssist provides a comprehensive and holistic means of evaluating human embryos. Instead of using a black-box algorithm, BlastAssist outputs meaningful measurements of embryos that can be interpreted and corroborated by embryologists, which is crucial in clinical decision making. Furthermore, the unprecedentedly large dataset generated by BlastAssist measurements can be used as a powerful resource for further research in human embryology and IVF. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by Harvard Quantitative Biology Initiative, the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number 1764269), the National Institute of Heath (award number R01HD104969), the Perelson Fund, and the Sagol fund for embryos and stem cells as part of the Sagol Network. The authors declare no competing interests. TRIAL REGISTRATION NUMBER: Not applicable.

Automatic removal of large blood vasculature for objective assessment of brain tumors using quantitative dynamic contrast-enhanced magnetic resonance imaging.

The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE-MRI data acquisition. Quantitative DCE-MRI analysis was performed for computing various parameters such as wash-out slope (Slope-2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (Vp), and volume transfer constant (Ktrans). An approach based on data-clustering algorithm, morphological operations, and quantitative DCE-MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data-clustering algorithms were evaluated on two types of quantitative maps: (a) Slope-2, and (b) a new proposed combination of rCBV and Slope-2 maps. Fluid-attenuated inversion recovery-MRI hyperintense lesions were also automatically segmented using deep learning-based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE-MRI parameters were obtained in the segmented tumor. t-test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k-means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high-grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low-grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and Vp maps show better visualization of tumor regions.

Epigenetic landscape reorganisation and reactivation of embryonic development genes are associated with malignancy in IDH-mutant astrocytoma.

Accurate grading of IDH-mutant gliomas defines patient prognosis and guides the treatment path. Histological grading is challenging, and aside from CDKN2A/B homozygous deletions in IDH-mutant astrocytomas, there are no other objective molecular markers used for grading. RNA-sequencing was conducted on primary IDH-mutant astrocytomas (n = 138) included in the prospective CATNON trial, which was performed to assess the prognostic effect of adjuvant and concurrent temozolomide. We integrated the RNA-sequencing data with matched DNA-methylation and NGS data. We also used multi-omics data from IDH-mutant astrocytomas included in the TCGA dataset and validated results on matched primary and recurrent samples from the GLASS-NL study. Since discrete classes do not adequately capture grading of these tumours, we utilised DNA-methylation profiles to generate a Continuous Grading Coefficient (CGC) based on classification scores from a CNS-tumour classifier. CGC was an independent predictor of survival outperforming current WHO-CNS5 and methylation-based classification. Our RNA-sequencing analysis revealed four distinct transcription clusters that were associated with (i) upregulation of cell cycling genes; (ii) downregulation of glial differentiation genes; (iii) upregulation of embryonic development genes (e.g. HOX, PAX, and TBX) and (iv) upregulation of extracellular matrix genes. The upregulation of embryonic development genes was associated with a specific increase of CpG island methylation near these genes. Higher grade IDH-mutant astrocytomas have DNA-methylation signatures that, on the RNA level, are associated with increased cell cycling, tumour cell de-differentiation and extracellular matrix remodelling. These combined molecular signatures can serve as an objective marker for grading of IDH-mutant astrocytomas.

Updates on grading mesothelioma.

Mesothelioma is a rare disease with an historically poor prognosis. Over the past decade, a grading system has been developed that is a powerful prognostic tool in epithelioid mesothelioma. Grading of epithelioid mesothelioma is now required or strongly recommended by expert consensus, the College of American Pathologists, the World Health Organization, and the International Mesothelioma Interest Group. The original nuclear grading system for epithelioid mesothelioma, developed in the United States, split epithelioid mesotheliomas into three prognostic groups with marked differences in survival. Now, this three-tiered nuclear grading system has been combined with the presence or absence of necrosis to form the currently recommended two-tiered grading system of low- and high-grade epithelioid mesothelioma. This review will focus on the development of this grading system in mesothelioma, the grading system's shortcomings, and the application of the grading system to cytology specimens and other extra-pleural sites. Lastly, this review will briefly discuss alternative grading systems and future considerations.

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia.

BACKGROUND: Diagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow-up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter- and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists. METHODS: In this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large-scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole-slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions that can be used to identify difficult cases. RESULTS: Our model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average area under the curve [AUC]: 0.878 (95% confidence interval [CI]: [0.834-0.918]) and 0.886 (95% CI: [0.813-0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions. CONCLUSION: Our results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying HN squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI-based assistive tools.

Head-to-head: Should Ki67 proliferation index be included in the formal classification of pulmonary neuroendocrine neoplasms?

The reporting of lung neuroendocrine neoplasms (NENs) according to the 2021 World Health Organisation (WHO) is based on mitotic count per 2 mm2, necrosis assessment and a constellation of cytological and immunohistochemical details. Accordingly, typical carcinoid and atypical carcinoid are low- to intermediate-grade neuroendocrine tumours (NETs), while large-cell neuroendocrine carcinoma (NEC) and small-cell lung carcinoma are high-grade NECs. In small-sized diagnostic material (cytology and biopsy), the noncommittal term of carcinoid tumour/NET not otherwise specified (NOS) and metastatic carcinoid NOS have been introduced with regard to primary and metastatic diagnostic settings, respectively. Ki-67 antigen, a well-known marker of cell proliferation, has been included in the WHO classification as a non-essential but desirable criterion, especially to distinguish NETs from high-grade NECs and to delineate the provisional category of carcinoid tumours/NETs with elevated mitotic counts (> 10 mitoses per mm2) and/or Ki-67 proliferation index (≥ 30%). However, a wider use of this marker in the spectrum of lung NENs continues to be highly reported and debated, thus witnessing a never-subsided attention. Therefore, the arguments for and against incorporating Ki-67 in the classification and clinical practice of these neoplasms are discussed herein in detail.

Updates on lung adenocarcinoma: invasive size, grading and STAS.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS: A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS: The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION: The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.